{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:58.447043Z",
     "start_time": "2018-09-07T02:50:55.850154Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl \n",
    "%matplotlib inline\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "import random as rn\n",
    "import os\n",
    "os.environ['PYTHONHASHSEED'] = '0'\n",
    "np.random.seed(42)\n",
    "rn.seed(12345)\n",
    "tf.set_random_seed(1234)\n",
    "\n",
    "# 设置中文编码和负号的正常显示\n",
    "plt.rcParams['font.sans-serif'] = 'simhei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "Path = 'D:\\\\APViaML'\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:59.059668Z",
     "start_time": "2018-09-07T02:50:58.449048Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_demo_dict_data():\n",
    "    file = open(Path + '\\\\data\\\\alldata_demo_top1000.pkl','rb')\n",
    "    raw_data = pickle.load(file)\n",
    "    file.close()\n",
    "    return raw_data\n",
    "\n",
    "data = get_demo_dict_data()\n",
    "top_1000_data_X = data['X']\n",
    "top_1000_data_Y = data['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:59.128851Z",
     "start_time": "2018-09-07T02:50:59.062676Z"
    }
   },
   "outputs": [],
   "source": [
    "X_factor_microlist = pd.read_excel(Path + '\\\\data\\\\List.xlsx',sheet_name='Charac')\n",
    "X_factor_microlist = X_factor_microlist['Acronym']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:59.140883Z",
     "start_time": "2018-09-07T02:50:59.130856Z"
    }
   },
   "outputs": [],
   "source": [
    "def creat_test_data(num,df_X=top_1000_data_X,df_Y=top_1000_data_Y):\n",
    "    \n",
    "    testdata_startyear_str = str(num + 1988) \n",
    "    X_testdata = np.array(df_X.loc[testdata_startyear_str])\n",
    "    Y_testdata = np.array(df_Y.loc[testdata_startyear_str])\n",
    "    return X_testdata, Y_testdata\n",
    "\n",
    "def Evaluation_fun(predict_array,real_array):\n",
    "    List1 = []\n",
    "    List2 = []\n",
    "    if len(predict_array) != len(real_array):\n",
    "        print('Something is worng!')\n",
    "    else:\n",
    "        for i in range(len(predict_array)):\n",
    "            List1.append(np.square(predict_array[i]-real_array[i]))\n",
    "            List2.append(np.square(real_array[i]))\n",
    "        result = round(100*(1 - sum(List1)/sum(List2)),3)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:59.156926Z",
     "start_time": "2018-09-07T02:50:59.142888Z"
    }
   },
   "outputs": [],
   "source": [
    "y_real = np.array(top_1000_data_Y.loc['1988':])\n",
    "col_list = np.array(top_1000_data_X.columns.values.tolist())\n",
    "col_list = col_list[:94]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:59.164949Z",
     "start_time": "2018-09-07T02:50:59.159934Z"
    }
   },
   "outputs": [],
   "source": [
    "# model_name = 'GBRT'\n",
    "# x = 'mve'\n",
    "# Y_pre_value = []\n",
    "# for n in  np.linspace(-1,1,11):\n",
    "#     for i in range(30):\n",
    "#         Y_pre_list_final = []\n",
    "#         testdata_startyear_str = str(i + 1988)\n",
    "#         X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "#         X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "#         for t in range(len(X_factor_microlist)):\n",
    "#             if X_factor_microlist[t] == x:\n",
    "#                 num2 = t\n",
    "#         num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "#         for z in range(len(col_list)):\n",
    "#             if col_list[z] == x:\n",
    "#                 num3 = z        \n",
    "#         num_list.append(num3)\n",
    "#         num_list.sort()\n",
    "\n",
    "#         X_testdata[:,num_list] = n\n",
    "#         model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'Model_'+model_name+'_Top1000_Prediction.pkl'\n",
    "#         file = open(model_filepath,'rb')\n",
    "#         best_model = pickle.load(file)\n",
    "#         file.close()        \n",
    "#         Y_pre_list =best_model.predict(X_testdata)\n",
    "#         temp_y_list = []\n",
    "#         for x in Y_pre_list:\n",
    "#             temp_y_list.append(x)\n",
    "#         Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "#     Y_pre_value.append(np.mean(Y_pre_list_final))\n",
    "        \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T02:50:59.173971Z",
     "start_time": "2018-09-07T02:50:59.169960Z"
    }
   },
   "outputs": [],
   "source": [
    "# Y_pre_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T03:07:37.676281Z",
     "start_time": "2018-09-07T02:50:59.176979Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n",
      "-0.8\n",
      "-0.6\n",
      "-0.3999999999999999\n",
      "-0.19999999999999996\n",
      "0.0\n",
      "0.20000000000000018\n",
      "0.40000000000000013\n",
      "0.6000000000000001\n",
      "0.8\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "from keras import backend as K\n",
    "K.clear_session()\n",
    "tf.reset_default_graph()\n",
    "import gc\n",
    "model_list2 = ['NN1','NN2','NN3']\n",
    "\n",
    "model_name = 'NN3'\n",
    "x = 'mve'\n",
    "Y_pre_value = []\n",
    "for n in  np.linspace(-1,1,11):\n",
    "    print(n)\n",
    "    for i in range(30):\n",
    "        Y_pre_list_final = []\n",
    "        testdata_startyear_str = str(i + 1988)\n",
    "        X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "        X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "        for t in range(len(X_factor_microlist)):\n",
    "            if X_factor_microlist[t] == x:\n",
    "                num2 = t\n",
    "        num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "        num_list.append(num2)\n",
    "        num_list.sort()\n",
    "\n",
    "        X_testdata[:,num_list] = n\n",
    "        model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'_Model_'+model_name+'_Top1000_Prediction.h5'\n",
    "        best_model = load_model(model_filepath )    \n",
    "        Y_pre_list =best_model.predict(X_testdata)\n",
    "        temp_y_list = []\n",
    "        for x in Y_pre_list[:,0]:\n",
    "            temp_y_list.append(x)\n",
    "        Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "        K.clear_session()\n",
    "        tf.reset_default_graph()\n",
    "        gc.collect()\n",
    "    Y_pre_value.append(np.mean(Y_pre_list_final))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T04:42:48.357130Z",
     "start_time": "2018-09-07T04:21:43.878186Z"
    },
    "code_folding": [
     7,
     15,
     58
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.029478627475267628]\n",
      "[-0.029478627475267628, -0.017916498758590558]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655, 0.02833201610811772]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655, 0.02833201610811772, 0.0398941448247948]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655, 0.02833201610811772, 0.0398941448247948, 0.051456273541471864]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655, 0.02833201610811772, 0.0398941448247948, 0.051456273541471864, 0.06301840225814893]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655, 0.02833201610811772, 0.0398941448247948, 0.051456273541471864, 0.06301840225814893, 0.074580530974826]\n",
      "[-0.029478627475267628, -0.017916498758590558, -0.006354370041913484, 0.0052077586747635895, 0.016769887391440655, 0.02833201610811772, 0.0398941448247948, 0.051456273541471864, 0.06301840225814893, 0.074580530974826, 0.08614265969150307]\n",
      "[[-0.02947863  0.          0.          0.        ]\n",
      " [-0.0179165   0.          0.          0.        ]\n",
      " [-0.00635437  0.          0.          0.        ]\n",
      " [ 0.00520776  0.          0.          0.        ]\n",
      " [ 0.01676989  0.          0.          0.        ]\n",
      " [ 0.02833202  0.          0.          0.        ]\n",
      " [ 0.03989414  0.          0.          0.        ]\n",
      " [ 0.05145627  0.          0.          0.        ]\n",
      " [ 0.0630184   0.          0.          0.        ]\n",
      " [ 0.07458053  0.          0.          0.        ]\n",
      " [ 0.08614266  0.          0.          0.        ]]\n",
      "[0.09427787101514479]\n",
      "[0.09427787101514479, 0.09630632794474143]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828, 0.09386453552246188]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828, 0.09386453552246188, 0.09420631925778859]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828, 0.09386453552246188, 0.09420631925778859, 0.09458915737519094]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828, 0.09386453552246188, 0.09420631925778859, 0.09458915737519094, 0.10543704069387846]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828, 0.09386453552246188, 0.09420631925778859, 0.09458915737519094, 0.10543704069387846, 0.11737717240164833]\n",
      "[0.09427787101514479, 0.09630632794474143, 0.0899922023002975, 0.08974323080152194, 0.08432785025546828, 0.09386453552246188, 0.09420631925778859, 0.09458915737519094, 0.10543704069387846, 0.11737717240164833, 0.11774894452337846]\n",
      "[[-0.02947863  0.09427787  0.          0.        ]\n",
      " [-0.0179165   0.09630633  0.          0.        ]\n",
      " [-0.00635437  0.0899922   0.          0.        ]\n",
      " [ 0.00520776  0.08974323  0.          0.        ]\n",
      " [ 0.01676989  0.08432785  0.          0.        ]\n",
      " [ 0.02833202  0.09386454  0.          0.        ]\n",
      " [ 0.03989414  0.09420632  0.          0.        ]\n",
      " [ 0.05145627  0.09458916  0.          0.        ]\n",
      " [ 0.0630184   0.10543704  0.          0.        ]\n",
      " [ 0.07458053  0.11737717  0.          0.        ]\n",
      " [ 0.08614266  0.11774894  0.          0.        ]]\n",
      "[0.047523271343690034]\n",
      "[0.047523271343690034, 0.05201650158290622]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801, 0.05222908886037752]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801, 0.05222908886037752, 0.0520426176054039]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801, 0.05222908886037752, 0.0520426176054039, 0.0537160971939608]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801, 0.05222908886037752, 0.0520426176054039, 0.0537160971939608, 0.05969099676412585]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801, 0.05222908886037752, 0.0520426176054039, 0.0537160971939608, 0.05969099676412585, 0.06605203598181097]\n",
      "[0.047523271343690034, 0.05201650158290622, 0.049685817457696534, 0.051663211623587585, 0.05056711404400801, 0.05222908886037752, 0.0520426176054039, 0.0537160971939608, 0.05969099676412585, 0.06605203598181097, 0.06605203598181097]\n",
      "[[-0.02947863  0.09427787  0.04752327  0.        ]\n",
      " [-0.0179165   0.09630633  0.0520165   0.        ]\n",
      " [-0.00635437  0.0899922   0.04968582  0.        ]\n",
      " [ 0.00520776  0.08974323  0.05166321  0.        ]\n",
      " [ 0.01676989  0.08432785  0.05056711  0.        ]\n",
      " [ 0.02833202  0.09386454  0.05222909  0.        ]\n",
      " [ 0.03989414  0.09420632  0.05204262  0.        ]\n",
      " [ 0.05145627  0.09458916  0.0537161   0.        ]\n",
      " [ 0.0630184   0.10543704  0.059691    0.        ]\n",
      " [ 0.07458053  0.11737717  0.06605204  0.        ]\n",
      " [ 0.08614266  0.11774894  0.06605204  0.        ]]\n"
     ]
    }
   ],
   "source": [
    "x='mve'\n",
    "## define which models we want to check\n",
    "model_list1 =['ENet','RF','GBRT']\n",
    "test_range = np.linspace(-1,1,11)\n",
    "mve = np.zeros(shape=(len(test_range),4))\n",
    "\n",
    "## run loop in 3 model\n",
    "for e,m in enumerate(model_list1):\n",
    "    model_name = str(m)\n",
    "    Y_pre_value = []\n",
    "\n",
    "    ## run loop in test factor range\n",
    "    for n in test_range:\n",
    "        Y_pre_list_final = []\n",
    "        #run loop in every year\n",
    "        for i in range(30):\n",
    "            testdata_startyear_str = str(i + 1988)\n",
    "            X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "            X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "            # get num2 to locate interations\n",
    "            for t in range(len(X_factor_microlist)):\n",
    "                if X_factor_microlist[t] == x:\n",
    "                    num2 = t\n",
    "            num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "\n",
    "            # get num3 to locate factor\n",
    "            for z in range(len(col_list)):\n",
    "                if col_list[z] == x:\n",
    "                    num3 = z        \n",
    "            num_list.append(num3)\n",
    "            num_list.sort()\n",
    "\n",
    "            # replace these factor to the value in test factor range\n",
    "            X_testdata[:,num_list] = n\n",
    "            model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'Model_'+model_name+'_Top1000_Prediction.pkl'\n",
    "            file = open(model_filepath,'rb')\n",
    "            best_model = pickle.load(file)\n",
    "            file.close()        \n",
    "            Y_pre_list =best_model.predict(X_testdata)\n",
    "\n",
    "            # get the prediction\n",
    "            temp_y_list = []\n",
    "            for x in Y_pre_list:\n",
    "                temp_y_list.append(x)\n",
    "            Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "            # we only care the mean value over_all years\n",
    "        Y_pre_value.append(np.mean(Y_pre_list_final))\n",
    "\n",
    "        print(Y_pre_value)\n",
    "     # save the result\n",
    "    mve[:,e] = Y_pre_value\n",
    "    print(mve)\n",
    "\n",
    "# the same for NN model ,difference in NN model have own saving type\n",
    "model_name = 'NN3'    \n",
    "Y_pre_value = []\n",
    "for n in  test_range:\n",
    "    Y_pre_list_final = []\n",
    "    for i in range(30):\n",
    "        testdata_startyear_str = str(i + 1988)\n",
    "        X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "        X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "        for t in range(len(X_factor_microlist)):\n",
    "            if X_factor_microlist[t] == x:\n",
    "                num2 = t\n",
    "        num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "        num_list.append(num2)\n",
    "        num_list.sort()\n",
    "\n",
    "        X_testdata[:,num_list] = n\n",
    "        model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'_Model_'+model_name+'_Top1000_Prediction.h5'\n",
    "        best_model = load_model(model_filepath )    \n",
    "        Y_pre_list =best_model.predict(X_testdata)\n",
    "        temp_y_list = []\n",
    "        for x in Y_pre_list[:,0]:\n",
    "            temp_y_list.append(x)\n",
    "        Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "        K.clear_session()\n",
    "        tf.reset_default_graph()\n",
    "        gc.collect()\n",
    "\n",
    "    Y_pre_value.append(np.mean(Y_pre_list_final))   \n",
    "mve[:,3] = Y_pre_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T05:15:48.140180Z",
     "start_time": "2018-09-07T04:54:45.149062Z"
    },
    "code_folding": [
     7,
     12,
     57
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02873014 0.         0.         0.        ]\n",
      " [0.02865051 0.         0.         0.        ]\n",
      " [0.02857089 0.         0.         0.        ]\n",
      " [0.02849126 0.         0.         0.        ]\n",
      " [0.02841164 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02825239 0.         0.         0.        ]\n",
      " [0.02817277 0.         0.         0.        ]\n",
      " [0.02809314 0.         0.         0.        ]\n",
      " [0.02801352 0.         0.         0.        ]\n",
      " [0.0279339  0.         0.         0.        ]]\n",
      "[[0.02873014 0.09309594 0.         0.        ]\n",
      " [0.02865051 0.09329594 0.         0.        ]\n",
      " [0.02857089 0.09335501 0.         0.        ]\n",
      " [0.02849126 0.09378266 0.         0.        ]\n",
      " [0.02841164 0.09386454 0.         0.        ]\n",
      " [0.02833202 0.09386454 0.         0.        ]\n",
      " [0.02825239 0.09386454 0.         0.        ]\n",
      " [0.02817277 0.09462524 0.         0.        ]\n",
      " [0.02809314 0.09462524 0.         0.        ]\n",
      " [0.02801352 0.09432304 0.         0.        ]\n",
      " [0.0279339  0.09432304 0.         0.        ]]\n",
      "[[0.02873014 0.09309594 0.05246904 0.        ]\n",
      " [0.02865051 0.09329594 0.05246904 0.        ]\n",
      " [0.02857089 0.09335501 0.05239009 0.        ]\n",
      " [0.02849126 0.09378266 0.05203666 0.        ]\n",
      " [0.02841164 0.09386454 0.05227276 0.        ]\n",
      " [0.02833202 0.09386454 0.05222909 0.        ]\n",
      " [0.02825239 0.09386454 0.05225391 0.        ]\n",
      " [0.02817277 0.09462524 0.05227267 0.        ]\n",
      " [0.02809314 0.09462524 0.05227267 0.        ]\n",
      " [0.02801352 0.09432304 0.05216927 0.        ]\n",
      " [0.0279339  0.09432304 0.05216927 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "x='mom12m'\n",
    "## define which models we want to check\n",
    "model_list1 =['ENet','RF','GBRT']\n",
    "test_range = np.linspace(-1,1,11)\n",
    "mom12m = np.zeros(shape=(len(test_range),4))\n",
    "\n",
    "## run loop in 3 model\n",
    "for e,m in enumerate(model_list1):\n",
    "    model_name = str(m)\n",
    "    Y_pre_value = []\n",
    "\n",
    "    ## run loop in test factor range\n",
    "    for n in test_range:\n",
    "        Y_pre_list_final = []\n",
    "        #run loop in every year\n",
    "        for i in range(30):\n",
    "            testdata_startyear_str = str(i + 1988)\n",
    "            X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "            X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "            # get num2 to locate interations\n",
    "            for t in range(len(X_factor_microlist)):\n",
    "                if X_factor_microlist[t] == x:\n",
    "                    num2 = t\n",
    "            num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "\n",
    "            # get num3 to locate factor\n",
    "            for z in range(len(col_list)):\n",
    "                if col_list[z] == x:\n",
    "                    num3 = z        \n",
    "            num_list.append(num3)\n",
    "            num_list.sort()\n",
    "\n",
    "            # replace these factor to the value in test factor range\n",
    "            X_testdata[:,num_list] = n\n",
    "            model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'Model_'+model_name+'_Top1000_Prediction.pkl'\n",
    "            file = open(model_filepath,'rb')\n",
    "            best_model = pickle.load(file)\n",
    "            file.close()        \n",
    "            Y_pre_list =best_model.predict(X_testdata)\n",
    "\n",
    "            # get the prediction\n",
    "            temp_y_list = []\n",
    "            for x in Y_pre_list:\n",
    "                temp_y_list.append(x)\n",
    "            Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "            # we only care the mean value over_all years\n",
    "        Y_pre_value.append(np.mean(Y_pre_list_final))\n",
    "\n",
    "     # save the result\n",
    "    mom12m[:,e] = Y_pre_value\n",
    "    print(mom12m)\n",
    "\n",
    "# the same for NN model ,difference in NN model have own saving type\n",
    "model_name = 'NN3'    \n",
    "Y_pre_value = []\n",
    "for n in  test_range:\n",
    "    Y_pre_list_final = []\n",
    "    for i in range(30):\n",
    "        testdata_startyear_str = str(i + 1988)\n",
    "        X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "        X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "        for t in range(len(X_factor_microlist)):\n",
    "            if X_factor_microlist[t] == x:\n",
    "                num2 = t\n",
    "        num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "        num_list.append(num2)\n",
    "        num_list.sort()\n",
    "\n",
    "        X_testdata[:,num_list] = n\n",
    "        model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'_Model_'+model_name+'_Top1000_Prediction.h5'\n",
    "        best_model = load_model(model_filepath )    \n",
    "        Y_pre_list =best_model.predict(X_testdata)\n",
    "        temp_y_list = []\n",
    "        for x in Y_pre_list[:,0]:\n",
    "            temp_y_list.append(x)\n",
    "        Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "        K.clear_session()\n",
    "        tf.reset_default_graph()\n",
    "        gc.collect()\n",
    "\n",
    "    Y_pre_value.append(np.mean(Y_pre_list_final))   \n",
    "mom12m[:,3] = Y_pre_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T05:36:20.321577Z",
     "start_time": "2018-09-07T05:15:50.608744Z"
    },
    "code_folding": [
     7,
     57
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02842226 0.         0.         0.        ]\n",
      " [0.02840421 0.         0.         0.        ]\n",
      " [0.02838616 0.         0.         0.        ]\n",
      " [0.02836811 0.         0.         0.        ]\n",
      " [0.02835006 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02831397 0.         0.         0.        ]\n",
      " [0.02829592 0.         0.         0.        ]\n",
      " [0.02827787 0.         0.         0.        ]\n",
      " [0.02825982 0.         0.         0.        ]\n",
      " [0.02824177 0.         0.         0.        ]]\n",
      "[[0.02842226 0.09081089 0.         0.        ]\n",
      " [0.02840421 0.09282458 0.         0.        ]\n",
      " [0.02838616 0.09283843 0.         0.        ]\n",
      " [0.02836811 0.0933778  0.         0.        ]\n",
      " [0.02835006 0.09386454 0.         0.        ]\n",
      " [0.02833202 0.09386454 0.         0.        ]\n",
      " [0.02831397 0.0962456  0.         0.        ]\n",
      " [0.02829592 0.09595454 0.         0.        ]\n",
      " [0.02827787 0.09595454 0.         0.        ]\n",
      " [0.02825982 0.09595454 0.         0.        ]\n",
      " [0.02824177 0.09590246 0.         0.        ]]\n",
      "[[0.02842226 0.09081089 0.05369734 0.        ]\n",
      " [0.02840421 0.09282458 0.05360805 0.        ]\n",
      " [0.02838616 0.09283843 0.0518548  0.        ]\n",
      " [0.02836811 0.0933778  0.05224383 0.        ]\n",
      " [0.02835006 0.09386454 0.05222909 0.        ]\n",
      " [0.02833202 0.09386454 0.05222909 0.        ]\n",
      " [0.02831397 0.0962456  0.05239194 0.        ]\n",
      " [0.02829592 0.09595454 0.05006684 0.        ]\n",
      " [0.02827787 0.09595454 0.05006684 0.        ]\n",
      " [0.02825982 0.09595454 0.05175162 0.        ]\n",
      " [0.02824177 0.09590246 0.05222859 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "x='retvol'\n",
    "## define which models we want to check\n",
    "model_list1 =['ENet','RF','GBRT']\n",
    "test_range = np.linspace(-1,1,11)\n",
    "retvol = np.zeros(shape=(len(test_range),4))\n",
    "\n",
    "## run loop in 3 model\n",
    "for e,m in enumerate(model_list1):\n",
    "    model_name = str(m)\n",
    "    Y_pre_value = []\n",
    "\n",
    "    ## run loop in test factor range\n",
    "    for n in test_range:\n",
    "        Y_pre_list_final = []\n",
    "        #run loop in every year\n",
    "        for i in range(30):\n",
    "            testdata_startyear_str = str(i + 1988)\n",
    "            X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "            X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "            # get num2 to locate interations\n",
    "            for t in range(len(X_factor_microlist)):\n",
    "                if X_factor_microlist[t] == x:\n",
    "                    num2 = t\n",
    "            num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "\n",
    "            # get num3 to locate factor\n",
    "            for z in range(len(col_list)):\n",
    "                if col_list[z] == x:\n",
    "                    num3 = z        \n",
    "            num_list.append(num3)\n",
    "            num_list.sort()\n",
    "\n",
    "            # replace these factor to the value in test factor range\n",
    "            X_testdata[:,num_list] = n\n",
    "            model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'Model_'+model_name+'_Top1000_Prediction.pkl'\n",
    "            file = open(model_filepath,'rb')\n",
    "            best_model = pickle.load(file)\n",
    "            file.close()        \n",
    "            Y_pre_list =best_model.predict(X_testdata)\n",
    "\n",
    "            # get the prediction\n",
    "            temp_y_list = []\n",
    "            for x in Y_pre_list:\n",
    "                temp_y_list.append(x)\n",
    "            Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "            # we only care the mean value over_all years\n",
    "        Y_pre_value.append(np.mean(Y_pre_list_final))\n",
    "\n",
    "     # save the result\n",
    "    retvol[:,e] = Y_pre_value\n",
    "    print(retvol)\n",
    "\n",
    "# the same for NN model ,difference in NN model have own saving type\n",
    "model_name = 'NN3'    \n",
    "Y_pre_value = []\n",
    "for n in  test_range:\n",
    "    Y_pre_list_final = []\n",
    "    for i in range(30):\n",
    "        testdata_startyear_str = str(i + 1988)\n",
    "        X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "        X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "        for t in range(len(X_factor_microlist)):\n",
    "            if X_factor_microlist[t] == x:\n",
    "                num2 = t\n",
    "        num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "        num_list.append(num2)\n",
    "        num_list.sort()\n",
    "\n",
    "        X_testdata[:,num_list] = n\n",
    "        model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'_Model_'+model_name+'_Top1000_Prediction.h5'\n",
    "        best_model = load_model(model_filepath )    \n",
    "        Y_pre_list =best_model.predict(X_testdata)\n",
    "        temp_y_list = []\n",
    "        for x in Y_pre_list[:,0]:\n",
    "            temp_y_list.append(x)\n",
    "        Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "        K.clear_session()\n",
    "        tf.reset_default_graph()\n",
    "        gc.collect()\n",
    "\n",
    "    Y_pre_value.append(np.mean(Y_pre_list_final))   \n",
    "retvol[:,3] = Y_pre_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-07T05:56:38.775472Z",
     "start_time": "2018-09-07T05:36:22.810194Z"
    },
    "code_folding": [
     7,
     57
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]\n",
      " [0.02833202 0.         0.         0.        ]]\n",
      "[[0.02833202 0.09241836 0.         0.        ]\n",
      " [0.02833202 0.09241836 0.         0.        ]\n",
      " [0.02833202 0.09241836 0.         0.        ]\n",
      " [0.02833202 0.0933742  0.         0.        ]\n",
      " [0.02833202 0.0933742  0.         0.        ]\n",
      " [0.02833202 0.09386454 0.         0.        ]\n",
      " [0.02833202 0.09407992 0.         0.        ]\n",
      " [0.02833202 0.09402433 0.         0.        ]\n",
      " [0.02833202 0.09341737 0.         0.        ]\n",
      " [0.02833202 0.09341737 0.         0.        ]\n",
      " [0.02833202 0.09341737 0.         0.        ]]\n",
      "[[0.02833202 0.09241836 0.05272055 0.        ]\n",
      " [0.02833202 0.09241836 0.05272055 0.        ]\n",
      " [0.02833202 0.09241836 0.05272055 0.        ]\n",
      " [0.02833202 0.0933742  0.05273068 0.        ]\n",
      " [0.02833202 0.0933742  0.0525308  0.        ]\n",
      " [0.02833202 0.09386454 0.05222909 0.        ]\n",
      " [0.02833202 0.09407992 0.05213467 0.        ]\n",
      " [0.02833202 0.09402433 0.0518895  0.        ]\n",
      " [0.02833202 0.09341737 0.05198168 0.        ]\n",
      " [0.02833202 0.09341737 0.05198168 0.        ]\n",
      " [0.02833202 0.09341737 0.05198168 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "x='acc'\n",
    "## define which models we want to check\n",
    "model_list1 =['ENet','RF','GBRT']\n",
    "test_range = np.linspace(-1,1,11)\n",
    "acc = np.zeros(shape=(len(test_range),4))\n",
    "\n",
    "## run loop in 3 model\n",
    "for e,m in enumerate(model_list1):\n",
    "    model_name = str(m)\n",
    "    Y_pre_value = []\n",
    "\n",
    "    ## run loop in test factor range\n",
    "    for n in test_range:\n",
    "        Y_pre_list_final = []\n",
    "        #run loop in every year\n",
    "        for i in range(30):\n",
    "            testdata_startyear_str = str(i + 1988)\n",
    "            X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "            X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "            # get num2 to locate interations\n",
    "            for t in range(len(X_factor_microlist)):\n",
    "                if X_factor_microlist[t] == x:\n",
    "                    num2 = t\n",
    "            num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "\n",
    "            # get num3 to locate factor\n",
    "            for z in range(len(col_list)):\n",
    "                if col_list[z] == x:\n",
    "                    num3 = z        \n",
    "            num_list.append(num3)\n",
    "            num_list.sort()\n",
    "\n",
    "            # replace these factor to the value in test factor range\n",
    "            X_testdata[:,num_list] = n\n",
    "            model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'Model_'+model_name+'_Top1000_Prediction.pkl'\n",
    "            file = open(model_filepath,'rb')\n",
    "            best_model = pickle.load(file)\n",
    "            file.close()        \n",
    "            Y_pre_list =best_model.predict(X_testdata)\n",
    "\n",
    "            # get the prediction\n",
    "            temp_y_list = []\n",
    "            for x in Y_pre_list:\n",
    "                temp_y_list.append(x)\n",
    "            Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "            # we only care the mean value over_all years\n",
    "        Y_pre_value.append(np.mean(Y_pre_list_final))\n",
    "\n",
    "     # save the result\n",
    "    acc[:,e] = Y_pre_value\n",
    "    print(acc)\n",
    "\n",
    "# the same for NN model ,difference in NN model have own saving type\n",
    "model_name = 'NN3'    \n",
    "Y_pre_value = []\n",
    "for n in  test_range:\n",
    "    Y_pre_list_final = []\n",
    "    for i in range(30):\n",
    "        testdata_startyear_str = str(i + 1988)\n",
    "        X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "        X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "        for t in range(len(X_factor_microlist)):\n",
    "            if X_factor_microlist[t] == x:\n",
    "                num2 = t\n",
    "        num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "        num_list.append(num2)\n",
    "        num_list.sort()\n",
    "\n",
    "        X_testdata[:,num_list] = n\n",
    "        model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'_Model_'+model_name+'_Top1000_Prediction.h5'\n",
    "        best_model = load_model(model_filepath )    \n",
    "        Y_pre_list =best_model.predict(X_testdata)\n",
    "        temp_y_list = []\n",
    "        for x in Y_pre_list[:,0]:\n",
    "            temp_y_list.append(x)\n",
    "        Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "        K.clear_session()\n",
    "        tf.reset_default_graph()\n",
    "        gc.collect()\n",
    "\n",
    "    Y_pre_value.append(np.mean(Y_pre_list_final))   \n",
    "acc[:,3] = Y_pre_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T11:53:38.033181Z",
     "start_time": "2018-09-08T11:53:37.118752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_factor = 'mve'\n",
    "model_list = ['ENet','RF','GBRT','NN3']\n",
    "plt.figure(figsize=(10,6))\n",
    "# for i in range(4):\n",
    "#     mve[:,i] = mve[:,i] - mve[5,i]\n",
    "for i,x in enumerate(model_list) :\n",
    "    model_name = x\n",
    "    marker_list = [\"o\",\"v\",\".\",\"x\"]\n",
    "    colors_list = ['green','blue','red','purple']\n",
    "    linestyle_list = ['-', '--', '-.', ':']\n",
    "\n",
    "    test_range = np.linspace(-1,1,11)\n",
    "    fig_x=test_range\n",
    "    y=mve[:,i]\n",
    "    # 绘图--1\n",
    "    plt.plot(fig_x, # x轴数据\n",
    "             y, # y轴数据\n",
    "             linestyle = linestyle_list[i], # 折线类型\n",
    "             linewidth = 2, # 折线宽度\n",
    "             color = colors_list[i], # 折线颜色\n",
    "             marker = marker_list[i], # 点的形状\n",
    "             markersize = 6, # 点的大小\n",
    "             markeredgecolor='black', # 点的边框色\n",
    "             markerfacecolor=colors_list[i], # 点的填充色\n",
    "             label = model_name) # 添加标签\n",
    "        # 添加阴影区间\n",
    "    plt.title(test_factor)\n",
    "    plt.grid(True,ls='--', which='major', axis='x')\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False) #去掉上边框\n",
    "    ax.spines['right'].set_visible(False) #去掉右边框\n",
    "    plt.savefig(Path + '\\\\output\\\\figure\\\\fig6'+ test_factor +'.jpg',bbox_inches = 'tight')\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(0.3,0.95),ncol=2,fancybox=True,shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T11:54:32.071872Z",
     "start_time": "2018-09-08T11:54:31.131370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_factor = 'mom12m'\n",
    "model_list = ['ENet','RF','GBRT','NN3']\n",
    "plt.figure(figsize=(10,6))\n",
    "for i in range(4):\n",
    "    mom12m[:,i] = mom12m[:,i] - mom12m[5,i]\n",
    "for i,x in enumerate(model_list) :\n",
    "    model_name = x\n",
    "    marker_list = [\"o\",\"v\",\".\",\"x\"]\n",
    "    colors_list = ['green','blue','red','purple']\n",
    "    linestyle_list = ['-', '--', '-.', ':']\n",
    "\n",
    "    test_range = np.linspace(-1,1,11)\n",
    "    fig_x=test_range\n",
    "    y=mom12m[:,i]\n",
    "    # 绘图--1\n",
    "    plt.plot(fig_x, # x轴数据\n",
    "             y, # y轴数据\n",
    "             linestyle = linestyle_list[i], # 折线类型\n",
    "             linewidth = 2, # 折线宽度\n",
    "             color = colors_list[i], # 折线颜色\n",
    "             marker = marker_list[i], # 点的形状\n",
    "             markersize = 6, # 点的大小\n",
    "             markeredgecolor='black', # 点的边框色\n",
    "             markerfacecolor=colors_list[i], # 点的填充色\n",
    "             label = model_name) # 添加标签\n",
    "        # 添加阴影区间\n",
    "    plt.title(test_factor)\n",
    "    plt.grid(True,ls='--', which='major', axis='x')\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False) #去掉上边框\n",
    "    ax.spines['right'].set_visible(False) #去掉右边框\n",
    "    plt.savefig(Path + '\\\\output\\\\figure\\\\fig6'+ test_factor +'.jpg',bbox_inches = 'tight')\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(0.3,0.95),ncol=2,fancybox=True,shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T11:55:18.523387Z",
     "start_time": "2018-09-08T11:55:17.620987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_factor = 'retvol'\n",
    "model_list = ['ENet','RF','GBRT','NN3']\n",
    "plt.figure(figsize=(10,6))\n",
    "for i in range(4):\n",
    "    retvol[:,i] = retvol[:,i] - retvol[5,i]\n",
    "for i,x in enumerate(model_list) :\n",
    "    model_name = x\n",
    "    marker_list = [\"o\",\"v\",\".\",\"x\"]\n",
    "    colors_list = ['green','blue','red','purple']\n",
    "    linestyle_list = ['-', '--', '-.', ':']\n",
    "\n",
    "    test_range = np.linspace(-1,1,11)\n",
    "    fig_x=test_range\n",
    "    y=retvol[:,i]\n",
    "    # 绘图--1\n",
    "    plt.plot(fig_x, # x轴数据\n",
    "             y, # y轴数据\n",
    "             linestyle = linestyle_list[i], # 折线类型\n",
    "             linewidth = 2, # 折线宽度\n",
    "             color = colors_list[i], # 折线颜色\n",
    "             marker = marker_list[i], # 点的形状\n",
    "             markersize = 6, # 点的大小\n",
    "             markeredgecolor='black', # 点的边框色\n",
    "             markerfacecolor=colors_list[i], # 点的填充色\n",
    "             label = model_name) # 添加标签\n",
    "        # 添加阴影区间\n",
    "    plt.title(test_factor)\n",
    "    plt.grid(True,ls='--', which='major', axis='x')\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False) #去掉上边框\n",
    "    ax.spines['right'].set_visible(False) #去掉右边框\n",
    "    plt.savefig(Path + '\\\\output\\\\figure\\\\fig6'+ test_factor +'.jpg',bbox_inches = 'tight')\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(0.3,0.95),ncol=2,fancybox=True,shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-08T11:55:39.370820Z",
     "start_time": "2018-09-08T11:55:38.490479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_factor = 'acc'\n",
    "model_list = ['ENet','RF','GBRT','NN3']\n",
    "plt.figure(figsize=(10,6))\n",
    "for i in range(4):\n",
    "    acc[:,i] = acc[:,i] - acc[5,i]\n",
    "for i,x in enumerate(model_list) :\n",
    "    model_name = x\n",
    "    marker_list = [\"o\",\"v\",\".\",\"x\"]\n",
    "    colors_list = ['green','blue','red','purple']\n",
    "    linestyle_list = ['-', '--', '-.', ':']\n",
    "\n",
    "    test_range = np.linspace(-1,1,11)\n",
    "    fig_x=test_range\n",
    "    y=acc[:,i]\n",
    "    # 绘图--1\n",
    "    plt.plot(fig_x, # x轴数据\n",
    "             y, # y轴数据\n",
    "             linestyle = linestyle_list[i], # 折线类型\n",
    "             linewidth = 2, # 折线宽度\n",
    "             color = colors_list[i], # 折线颜色\n",
    "             marker = marker_list[i], # 点的形状\n",
    "             markersize = 6, # 点的大小\n",
    "             markeredgecolor='black', # 点的边框色\n",
    "             markerfacecolor=colors_list[i], # 点的填充色\n",
    "             label = model_name) # 添加标签\n",
    "        # 添加阴影区间\n",
    "    plt.title(test_factor)\n",
    "    plt.grid(True,ls='--', which='major', axis='x')\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False) #去掉上边框\n",
    "    ax.spines['right'].set_visible(False) #去掉右边框\n",
    "    plt.savefig(Path + '\\\\output\\\\figure\\\\fig6'+ test_factor +'.jpg',bbox_inches = 'tight')\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(0.3,0.95),ncol=2,fancybox=True,shadow=True)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2018-09-07T03:08:05.842174Z",
     "start_time": "2018-09-07T02:50:55.888Z"
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   "outputs": [],
   "source": [
    "# x = 'mom12m'\n",
    "\n",
    "# model_list1 =['ENet','RF','GBRT']\n",
    "# test_range = np.linspace(-1,1,11)\n",
    "# mve = np.zeros(shape=(len(test_range),4))\n",
    "# for e,m in enumerate(model_list1):\n",
    "#     model_name = str(m)\n",
    "#     Y_pre_value = []\n",
    "#     for n in  test_range:\n",
    "#         for i in range(30):\n",
    "#             Y_pre_list_final = []\n",
    "#             testdata_startyear_str = str(i + 1988)\n",
    "#             X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "#             X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "#             for t in range(len(X_factor_microlist)):\n",
    "#                 if X_factor_microlist[t] == x:\n",
    "#                     num2 = t\n",
    "#             num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "#             for z in range(len(col_list)):\n",
    "#                 if col_list[z] == x:\n",
    "#                     num3 = z        \n",
    "#             num_list.append(num3)\n",
    "#             num_list.sort()\n",
    "\n",
    "#             X_testdata[:,num_list] = n\n",
    "#             model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'Model_'+model_name+'_Top1000_Prediction.pkl'\n",
    "#             file = open(model_filepath,'rb')\n",
    "#             best_model = pickle.load(file)\n",
    "#             file.close()        \n",
    "#             Y_pre_list =best_model.predict(X_testdata)\n",
    "#             temp_y_list = []\n",
    "#             for x in Y_pre_list:\n",
    "#                 temp_y_list.append(x)\n",
    "#             Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "#         Y_pre_value.append(np.mean(Y_pre_list_final))\n",
    "#     mve[:,e] = Y_pre_value\n",
    "\n",
    "# model_name = 'NN3'    \n",
    "# Y_pre_value = []\n",
    "# for n in  test_range:\n",
    "#     Y_pre_value = []\n",
    "#     for i in range(30):\n",
    "#         Y_pre_list_final = []\n",
    "#         testdata_startyear_str = str(i + 1988)\n",
    "#         X = np.array(top_1000_data_X.loc[testdata_startyear_str])\n",
    "#         X_testdata = np.zeros(shape=(len(X),len(X[0])))\n",
    "\n",
    "#         for t in range(len(X_factor_microlist)):\n",
    "#             if X_factor_microlist[t] == x:\n",
    "#                 num2 = t\n",
    "#         num_list = list(range(num2*8+94,(num2+1)*8+94))\n",
    "#         num_list.append(num2)\n",
    "#         num_list.sort()\n",
    "\n",
    "#         X_testdata[:,num_list] = n\n",
    "#         model_filepath = Path + '\\\\model\\\\' + model_name+'\\\\'+ str(i+1988)+'_Model_'+model_name+'_Top1000_Prediction.h5'\n",
    "#         best_model = load_model(model_filepath )    \n",
    "#         Y_pre_list =best_model.predict(X_testdata)\n",
    "#         temp_y_list = []\n",
    "#         for x in Y_pre_list[:,0]:\n",
    "#             temp_y_list.append(x)\n",
    "#         Y_pre_list_final =  Y_pre_list_final +  temp_y_list\n",
    "\n",
    "#     Y_pre_value.append(np.mean(Y_pre_list_final))   \n",
    "# mve[:,3] = Y_pre_value"
   ]
  }
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